System and method for controlling product recommendations

Abstract

System and methods for controlling product recommendations are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical customer data associated with a plurality of customers, generating, based on the historical customer data, journey data associated with the customer, determining, based on real-time interaction data, that the customer is interacting with a first product associated with a first category, and generating a cross-pollinating intent score based on the journey data and the real-time interaction data.

What is claimed is: 
     
         1 . A system, comprising:
 a database storing historical customer data associated with a plurality of customers;   a computing device comprising at least one processor in communication with the database, the computing device being configured to:   generate, based on the historical customer data, journey data associated with the customer;   determine, based on real-time interaction data, that the customer is interacting with a first product associated with a first category; and   generate a cross-pollinating intent score based on the journey data and the real-time interaction data.   
     
     
         2 . The system of  claim 1 , wherein the computing device is further configured to:
 identify, based on the journey data, a plurality of cross-pollinating products, the cross-pollinating products being in a cross-pollinating category different than the first category;   generate an affinity score for each of the plurality of cross-pollinating products;   prioritize each of the plurality of cross-pollinating products based on their respective affinity scores; and   display, on a user interface, based on the prioritization, the plurality of cross-pollinating products in a specific arrangement.   
     
     
         3 . The system of  claim 2 , wherein the affinity score is dependent on one or more of a brand affinity score, a product type affinity score, a price affinity score, and a relevance score. 
     
     
         4 . The system of  claim 2 , wherein the affinity score is dependent the cross-pollinating intent score. 
     
     
         5 . The system of  claim 2 , wherein the affinity score is generated by a machine learning model that is evaluated and refined. 
     
     
         6 . The system of  claim 5 , wherein the machine learning model undergoes incremental training at a regularly set time frame to cause refinement of the machine learning model. 
     
     
         7 . The system of  claim 2 , wherein the computing device is further configured to:
 generate a comparison between two or more cross-pollinating products;   based on the comparison, iteratively manipulate one or more weights associated with the affinity score; and   generate an updated affinity score based on the manipulated one or more weights.   
     
     
         8 . The system of  claim 1 , wherein the cross-pollinating intent score is a probability that the customer interacts with a second product associated with a second category different than the first category. 
     
     
         9 . The system of  claim 1  further comprising:
 a user interface configured to display a plurality of cross-pollinating products in a prioritized arrangement. 
 
     
     
         10 . The system of  claim 1 , wherein the historical customer data includes profile data associated with the journey data. 
     
     
         11 . A method comprising:
 storing, in a database, historical customer data associated with a plurality of customers;   generating, based on the historical customer data, journey data associated with the customer;   determining, based on real-time interaction data, that the customer is interacting with a first product associated with a first category; and   generating a cross-pollinating intent score based on the journey data and the real-time interaction data.   
     
     
         12 . The method of  claim 11  further comprising:
 identifying, based on the journey data, a plurality of cross-pollinating products, the cross-pollinating products being in a cross-pollinating category different than the first category; 
 generating an affinity score for each of the plurality of cross-pollinating products; 
 prioritizing each of the plurality of cross-pollinating products based on their respective affinity scores; and 
 displaying, on a user interface, based on the prioritization, the plurality of cross-pollinating products in a specific arrangement. 
 
     
     
         13 . The method of  claim 12 , wherein the affinity score is dependent on one or more of a brand affinity score, a product type affinity score, a price affinity score, and a relevance score. 
     
     
         14 . The method of  claim 12 , wherein the affinity score is dependent the cross-pollinating intent score. 
     
     
         15 . The method of  claim 12 , wherein the affinity score is generated by a machine learning model that is evaluated and refined. 
     
     
         16 . The method of  claim 15 , wherein the machine learning model undergoes incremental training at a regularly set time frame to cause refinement of the machine learning model. 
     
     
         17 . The method of  claim 12 , wherein the method further includes:
 generating a comparison between two or more cross-pollinating products;   based on the comparison, iteratively manipulating one or more weights associated with the affinity score; and   generating an updated affinity score based on the manipulated one or more weights.   
     
     
         18 . The method of  claim 11 , wherein the cross-pollinating intent score is a probability that the customer interacts with a second product associated with a second category different than the first category. 
     
     
         19 . The method of  claim 11  further comprising:
 displaying, on a user interface, a plurality of cross-pollinating products in a prioritized arrangement. 
 
     
     
         20 . A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:
 storing, in a database, historical customer data associated with a plurality of customers;   generating, based on the historical customer data, journey data associated with the customer;   determining, based on real-time interaction data, that the customer is interacting with a first product associated with a first category; and   generating a cross-pollinating intent score based on the journey data and the real-time interaction data in a specific arrangement.